Finite Mixture Modeling via Skew-Laplace Birnbaum–Saunders Distribution
Mehrdad Naderi, Mahdieh Mozafari, Kheirolah Okhli · Journal of Statistical Theory and Applications · 2020
Finite mixture model is a widely acknowledged model-based clustering method for analyzing data.In this paper, a new finite mixture model via an extension of Birnbaum-Saunders distribution is introduced.The new mixture model provide a useful generalization of the heavy-tailed lifetime model since the mixing components cover both skewness and kurtosis.Some properties and characteristics of the model are derived and an expectation and maximization (EM)-type algorithm is developed to compute maximum likelihood estimates.The asymptotic standard errors of the parameter estimates are obtained via offering an information-based approach.Finally, the performance of the methodology is illustrated by considering both simulated and real datasets.